针对风能转换系统执行器部分失效故障,提出了一种新型的主动容错控制策略.应用径向基函数(radial basis function,RBF)自适应神经网络,根据系统状态观测值对执行器故障进行在线重构,基于该重构故障,设计滑模容错控制器切换增益,实现风能转换系统故障诊断与容错控制律在线整定,并进行稳定性证明.仿真结果表明,执行器发生故障时系统的功率系数和叶尖速比均能保持在最优值,从而实现额定风速以下的最大风能捕获. A new active fault tolerant control strategy is proposed for partial failure of actuators in wind energy conversion system. The radial basis function (RBF) adaptive neural network is used to perform online fault Based on this reconstruction fault, the sliding-mode fault-tolerant controller is designed to switch the gain, to realize the fault diagnosis of wind energy conversion system and online fault-tolerant control law, and to prove the stability.The simulation results show that the power coefficient and Tip speed ratio can be maintained at the optimal value, so as to achieve the maximum wind energy below the rated wind speed capture.